The goal of this software is to determine the solution of an operational route planning problem under uncertainty for Demand Adaptive Systems.
This software contains four folders: data, generators, results, solver.
generators: contains data for the case study of our paper.helper: helps to load a scenario.results: information for runs gets saved heresolver: contains three solvers, i.e., our exact decomposition, heuristic, and greedy approaches
The results in the paper were generated by this software that had been carried out using Python 3.8.11 and Gurobi 9.5 on a desktop computer with Intel(R) Core(TM) i9-9900, 3.1 GHz CPU and 4 GB of RAM, running Ubuntu 20.04.
To replicate the results of an instance of our case study run python ./run_all.py [route] [route_time] [comp_perc] [num_scenarios] [walking_distance] [util_value] [algorithm] [seed].
The following input arguments are valid:
| Argument | Inputs |
|---|---|
| route | [55, 155] |
| route_time | for 55: '18_56_00' or '15_58_00'; for 155: '13_50_00' or '07_44_30' |
| comp_perc | 0 <= comp_perc <= 1 |
| num_scenarios | 0 <= num_scenarios <= 40 |
| walking_distance | [100, 150, 250, 300, 350, 400] |
| util_value | [500, 750, 1000] |
| algorithm | ['exact', 'heuristic', 'greedy'] |
| seed | positive integer |
Running an instance from route 55, at route_time 18_56_00, comp_perc 0.9, num_scenarios 5, walking_distance 250, util_value 750, algorithm 'exact', and seed 5 can be done via
python .\run_all.py 55 18_56_00 0.9 5 250 750 'exact' 5